Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

QoS-Aware Orchestration of Network Intensive Software Utilities within Software Defined Data Centres

An Architecture and Implementation of a Global Cluster Manager

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

Although the cloud computing domain is progressing rapidly, the deployment of various network intensive software utilities in the cloud is still a challenging task. The Quality of Service (QoS) for various gaming, simulations, videoconferencing, video streaming or even file uploading tasks may be significantly affected by the quality and geolocation of the selected underlying computing resources, which are available only when the specific functionality is required. This study presents a new architecture for geographic orchestration of network intensive software components which is designed for high QoS. Key elements of this architecture are a Global Cluster Manager operating within Software-Defined Data Centres (SDDCs), a runtime QoS Monitoring System, and a QoS Modeller and Decision Maker for automated orchestration of software utilities. The implemented system automatically selects the best geographically available computing resource within the SDDC according to the developed QoS model of the software component. This architecture is event-driven as the services are deployed and destroyed in real-time for every usage event. The utility of the implemented orchestration technology is verified qualitatively and in relation to the potential gains of selected QoS metrics by using two network intensive software utilities implemented as containers: an HTTP(S) File Upload service and a Jitsi Meet videoconferencing service. The study shows potential for QoS improvements in comparison to existing orchestration systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Žabkar, J., Žabkar, R., Vladušič, D., Čemas, D., Šuc, D., Bratko, I.: Q 2 prediction of ozone concentrations. Ecol. Model. 191(1), 68–82 (2006). https://doi.org/10.1016/j.ecolmodel.2005.08.013. Selected Papers from the Fourth International Workshop on Environmental Applications of Machine Learning, September 27–October 1, 2004, Bled, Slovenia. http://www.sciencedirect.com/science/article/pii/S0304380005003509

    Article  Google Scholar 

  2. Ahonen, J.J.: On qualitative modelling. AI & Soc. 8(1), 17–28 (1994). https://doi.org/10.1007/BF02065175

    Article  Google Scholar 

  3. Avetisyan, A.I., Campbell, R., Gupta, I., Heath, M.T., Ko, S.Y., Ganger, G.R., Kozuch, M.A., O’Hallaron, D., Kunze, M., Kwan, T.T., Lai, K., Lyons, M., Milojicic, D.S., Lee, H.Y., Soh, Y.C., Ming, N.K., Luke, J.Y., Namgoong, H.: Open cirrus: a global cloud computing testbed. Computer 43(4), 35–43 (2010). https://doi.org/10.1109/MC.2010.111

    Article  Google Scholar 

  4. Bari, M.F., Chowdhury, S.R., Ahmed, R., Boutaba, R.: Policycop: an autonomic qos policy enforcement framework for software defined networks. In: 2013 IEEE SDN for Future Networks and Services (SDN4FNS), pp 1–7 (2013). https://doi.org/10.1109/SDN4FNS.2013.6702548

  5. Baxley, K., la Rosa, J.D., Wenning, M.: Deploying workloads with juju and maas in ubuntu 14.04 lts. http://docplayer.net/12356952-Solution-brief-ca-service-management-service-catalog-can-we-manage-and-deliver-the-services-needed-where-when-and-how-our-users-need-them.html. A Dell Technical White paper (2014)

  6. Berndtsson, G., Folkesson, M., Kulyk, V.: Subjective quality assessment of video conferences and telemeetings. In: Proceedings of the 19th International Packet Video Workshop (PV), pp 25–30. IEEE, Piscataway (2012)

  7. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog Computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: a Roadmap for Smart Environments, pp. 169–186. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7

  8. Bratko, I., Suc, D.: Learning qualitative models. AI Mag. 24(4), 107 (2003)

    Google Scholar 

  9. Buyya, R., Calheiros, R.N., Son, J., Dastjerdi, A.V., Yoon, Y.: Software-defined cloud computing: architectural elements and open challenges. arXiv:1408.6891 (2014)

  10. Carvalho, J.P., Tome, J.A.B.: Qualitative modelling of an economic system using rule-based fuzzy cognitive maps. In: 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542). https://doi.org/10.1109/FUZZY.2004.1375476, vol. 2, pp 659–664 (2004)

  11. Chang, V., Ramachandran, M.: Financial modeling and prediction as a service. J. Grid Comput. 15(2), 177–195 (2017). https://doi.org/10.1007/s10723-017-9393-3

    Article  Google Scholar 

  12. Cheng, B.H.C., Eder, K.I., Gogolla, M., Grunske, L., Litoiu, M., Müller, H.A., Pelliccione, P., Perini, A., Qureshi, N.A., Rumpe, B., Schneider, D., Trollmann, F., Villegas, N.M.: Using Models at Runtime to Address Assurance for Self-Adaptive Systems, pp 101–136. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-08915-7_4

    Google Scholar 

  13. Chowdhury, N.M.K., Boutaba, R.: A survey of network virtualization. Comput. Netw. 54(5), 862–876 (2010). https://doi.org/10.1016/j.comnet.2009.10.017

    Article  MATH  Google Scholar 

  14. Devops: devops official web page. https://devops.com/ (2017). Accessed August 2017

  15. Fabric8: fabric8 documentation. http://fabric8.io/guide/overview.html (2017). Accessed August 2017

  16. Fiedler, M., Hossfeld, T., Tran-Gia, P.: A generic quantitative relationship between quality of experience and quality of service. IEEE Netw. 24(2), 36–41 (2010). https://doi.org/10.1109/MNET.2010.5430142

    Article  Google Scholar 

  17. Forbus, K.D.: Qualitative modeling. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation, Chap. 9, pp 361–393. Elsevier B. V., Amsterdam (2008)

  18. Gec, S., Kimovski, D., Paščinski, U., Prodan, R., Stankovski, V.: Semantic approach for multi-objective optimisation of the entice distributed virtual machine and container images repository. Concurrency and Computation: Practice and Experience, pp. e4264–n/a (2017). https://doi.org/10.1002/cpe.4264

  19. Heidari, P., Lemieux, Y., Shami, A.: Qos assurance with light virtualization - a survey. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (Cloudcom), pp 558–563 (2016). https://doi.org/10.1109/CloudCom.2016.0097

  20. Hoque, S., de Brito, M.S., Willner, A., Keil, O., Magedanz, T.: Towards container orchestration in fog computing infrastructures. In: 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC) vol. 2, pp. 294–299 (2017). https://doi.org/10.1109/COMPSAC.2017.248

  21. Huebscher, M.C., McCann, J.A.: A survey of autonomic computing—degrees, models, and applications. ACM Comput. Surv. 40(3), 7:1–7:28 (2008). https://doi.org/10.1145/1380584.1380585

    Article  Google Scholar 

  22. ITU-T: P.1301 : subjective quality evaluation of audio and audiovisual multiparty telemeetings. Recommendation P.1301, International Telecommunication Union, Geneva (2012)

  23. Jamshidi, P., Pahl, C., Mendonca, N.C.: Managing uncertainty in autonomic cloud elasticity controllers. IEEE Cloud Comput. 3(3), 50–60 (2016). https://doi.org/10.1109/MCC.2016.66

    Article  Google Scholar 

  24. Jifeng, H., Li, X., Liu, Z.: Component-based software engineering. In: Van Hung, D., Wirsing, M. (eds.) Theoretical Aspects of Computing – ICTAC 2005: Second International Colloquium, Hanoi, Vietnam, October 17–21, 2005. Proceedings, pp 70–95. Springer, Berlin (2005). https://doi.org/10.1007/11560647_5

  25. Kacsuk, P., Kecskemeti, G., Kertesz, A., Nemeth, Z., Kovács, J., Farkas, Z.: Infrastructure aware scientific workflows and infrastructure aware workflow managers in science gateways. J. Grid Comput. 14(4), 641–654 (2016). https://doi.org/10.1007/s10723-016-9380-0

    Article  Google Scholar 

  26. Kliazovich, D., Pecero, J.E., Tchernykh, A., Bouvry, P., Khan, S.U., Zomaya, A.Y.: CA-DAG: modeling communication-aware applications for scheduling in cloud computing. J. Grid Comput. 14(1), 23–39 (2016). https://doi.org/10.1007/s10723-015-9337-8

    Article  Google Scholar 

  27. Kornyshova, E., Deneckère, R.: Using an ontology for modeling decision-making knowledge, pp. 1553–1562 (2012). https://doi.org/10.3233/978-1-61499-105-2-1553

  28. Liu, C., Van Der Merwe, J., Mao, Y., Fernández, M.: Cloud resource orchestration: a data-centric approach. In: Proceedings of the 5th Biennial Conference on Innovative Data Systems Research, CIDR 2011, pp 241–248 (2011)

  29. Liu, H., Parashar, M., Hariri, S.: A component-based programming model for autonomic applications. In: Proceedings of the International Conference on Autonomic Computing, 2004, pp. 10–17 (2004). https://doi.org/10.1109/ICAC.2004.1301341

  30. López-Pires, F., Barán, B.: Many-objective virtual machine placement. J. Grid Comput. 15(2), 161–176 (2017). https://doi.org/10.1007/s10723-017-9399-x

    Article  Google Scholar 

  31. Lu, Y., Wang, F., Jia, M., Qi, Y.: Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters. Mech. Syst. Signal Process. 81, 259–273 (2016)

    Article  Google Scholar 

  32. Lunze, J.: Qualitative modelling of linear dynamical systems with quantized state measurements. Automatica 30(3), 417–431 (1994). https://doi.org/10.1016/0005-1098(94)90119-8. http://www.sciencedirect.com/science/article/pii/0005109894901198

    Article  MathSciNet  MATH  Google Scholar 

  33. Pahl, C., Lee, B.: Containers and clusters for edge cloud architectures – a technology review. In: 2015 3rd International Conference on Future Internet of Things and Cloud, pp. 379–386 (2015). https://doi.org/10.1109/FiCloud.2015.35

  34. Shila, D.M., Shen, W., Cheng, Y., Tian, X., Shen, X.S.: Amcloud: toward a secure autonomic mobile ad hoc cloud computing system. IEEE Wirel. Commun. 24(2), 74–81 (2017). https://doi.org/10.1109/MWC.2016.1500119RP

    Article  Google Scholar 

  35. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016). https://doi.org/10.1007/s10723-015-9359-2

    Article  Google Scholar 

  36. Software: autonomous self-adaptation platform. https://hub.docker.com/r/jernejtrnkoczy/jcontrolagent01 (2017)

  37. Software: docker official web page. https://www.docker.com/ (2017)

  38. Software: Jitsi meet docker container. https://hub.docker.com/r/jernejtrnkoczy/jitsimeet004/ (2017)

  39. Software: Kubernetes. https://kubernetes.io/ (2017)

  40. Software: Netdata. https://github.com/firehol/netdata (2017)

  41. Sun, Y., White, J., Eade, S., Schmidt, D.C.: ROAR: a QoS-oriented modeling framework for automated cloud resource allocation and optimization. J. Syst. Softw. 116, 146–161 (2016). https://doi.org/10.1016/j.jss.2015.08.006

    Article  Google Scholar 

  42. Taherizadeh, S., Ian, T., Jones, A., Zhao, Z., Stankovski, V.: A network edge monitoring approach for real-time data streaming applications. In: Proceedings of the 13th International Conference on Economics of Grids, Clouds, Systems and Services (GECON), p 2016. ACM, Athens (2016)

  43. Taherizadeh, S., Stankovski, V.: Quality of service assurance for internet of things time-critical cloud applications. In: Proceedings of the 6th International Congress on Advanced Applied Informatics (AAI 2017) (2017)

  44. Taherizadeh, S., Taylor, I., Jones, A., Zhao, Z., Stankovski, V.: A Network Edge Monitoring Approach for Real-Time Data Streaming Applications, pp 293–303. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-61920-0_21

    Google Scholar 

  45. Toosi, A.N., Calheiros, R.N., Buyya, R.: Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput. Surv. 47(1), 7:1–7:47 (2014). https://doi.org/10.1145/2593512

    Article  Google Scholar 

  46. Trihinas, D., Sofokleous, C., Loulloudes, N., Foudoulis, A., Pallis, G., Dikaiakos, M.D.: Managing and Monitoring Elastic Cloud Applications, pp 523–527. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-08245-5_42

    Google Scholar 

  47. Vladusic, D., Kompare, B., Bratko, I.: Modelling lake glumso with q2 learning. Ecol. Model. 191, 33–46 (2006)

    Article  Google Scholar 

  48. Wang, J., Taal, A., Martin, P., Hu, Y., Zhou, H., Pang, J., de Laat, C., Zhao, Z.: Planning virtual infrastructures for time critical applications with multiple deadline constraints. Futur. Gener. Comput. Syst. 75, 365–375 (2017). https://doi.org/10.1016/j.future.2017.02.001. http://www.sciencedirect.com/science/article/pii/S0167739X17301905

    Article  Google Scholar 

  49. Weerasiri, D., Barukh, M.C., Benatallah, B., Sheng, Q.Z., Ranjan, R.: A taxonomy and survey of cloud resource orchestration techniques. ACM Comput. Surv. 50(2), 26:1–26:41 (2017). https://doi.org/10.1145/3054177

    Article  Google Scholar 

  50. Wikipage: Linux foundation wiki web page. https://wiki.linuxfoundation.org/networking/netem (2017)

  51. Winkler, S., Mohandas, P.: The evolution of video quality measurement: From psnr to hybrid metrics. IEEE Trans. Broadcast. 54(3), 660–668 (2008). https://doi.org/10.1109/TBC.2008.2000733

    Article  Google Scholar 

  52. Xiong, P., Pu, C., Zhu, X., Griffith, R.: Vperfguard: an automated model-driven framework for application performance diagnosis in consolidated cloud environments. In: Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering, pp 271–282. ACM, New York (2013)

  53. Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47(4), 63:1–63:33 (2015). https://doi.org/10.1145/2788397

    Article  Google Scholar 

Download references

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 643963 (SWITCH project: Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications) and under grant agreement no. 644179 (ENTICE project: dEcentralised repositories for traNsparent and efficienT vIrtual maChine opErations). The authors are thankful to the Academic and Research Network of Slovenia (ARNES) for using their public cloud infrastructure.

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Uroš Paščinski, Matej Cigale & Sandi Gec

  2. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia

    Jernej Trnkoczy

  3. School of Computer Science, Cardiff University, Cardiff, UK

    Matej Cigale

  4. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, 1000, Ljubljana, Slovenia

    Vlado Stankovski

Authors
  1. Uroš Paščinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jernej Trnkoczy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vlado Stankovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matej Cigale
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandi Gec
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vlado Stankovski.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paščinski, U., Trnkoczy, J., Stankovski, V. et al. QoS-Aware Orchestration of Network Intensive Software Utilities within Software Defined Data Centres. J Grid Computing 16, 85–112 (2018). https://doi.org/10.1007/s10723-017-9415-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-017-9415-1

Keywords

Search

Navigation